A Systematic Review of Developing a Comprehensive and Strategic Framework for Business Model Innovation and Adaptation Amid Technology and Market-Driven Disruption for Incumbent Businesses

Abstract

Purpose – This study aims to address a crucial gap in academic understanding regarding business-model innovation and adaptation, particularly within the dynamic landscape of technology and IT-based companies experiencing disruptive innovations. To meet the increased demand for a precise approach, timely execution, and relevant content in business-model evolution, this study strives to develop a comprehensive framework. Through synthesizing existing knowledge, the goal is to contribute valuable insights and offer a cohesive guide for businesses navigating the complexities of innovating and adapting their business models amidst technological disruptions.Design/Methodology/Approach – Through a systematic review of 29 research articles from reputable journals, we employ a structured, five-step approach to define research questions, conduct literature search, select relevant studies, rigorously extract and synthesize data, and report findings. Emphasizing methodological rigor, consistent with established practices, this approach provides a robust foundation for developing a strategic framework for business model evolution amid disruptions.Findings – This study contributes significantly by developing a comprehensive and integrated framework to help incumbent businesses navigate disrupter threats and implement business-model innovation. The synthesis of the relevant literature and practical guidance empowers incumbent businesses for long-term competitiveness.Research Implications – This study has substantial implications, offering academia a comprehensive and integrated strategic framework for navigating disrupter threats through business-model innovation. It provides practical guidance for incumbent businesses, aiding in strategic decision-making and competitiveness. The findings set the stage for future research to explore industry-specific contexts and advance methods for early disrupter detection in the evolving landscape of technology disruptions.